Energy Production from Cattle Manure within a Life Cycle Assessment Framework: Statistical Optimization of Co-Digestion, Pretreatment, and Thermal Conditions
Bibliographic record
Abstract
Waste to energy processes from anaerobic digestion (WtE-AD) from cattle manure (CM) have low CH4 yields due to CM’s structural composition. The search for alternatives to increase the energy yields of these processes must consider the optimization of operating parameters within a framework of mitigating the environmental footprint. The goal of this paper is to provide a statistical optimization strategy based on experimental designs to improve CH4 yields and reduce the environmental profile of CM valorization through a WtE-AD process. Biochemical methane potential tests were conducted to determine the energetic and environmental effects that alkaline pretreatments, different AD temperatures, and co-digestion formulations with fruit and vegetable waste (FVW) have on the WtE-AD process from CM. The evaluation was conducted following a life cycle assessment approach through energy balances. The results indicate that the highest CH4 yield (384.3 mL CH4 g VS−1) and the lowest environmental impact (−0.06 kg CO2 eq kWh−1 of electricity production) were achieved with the co-digestion of CM with FVW 1:1, pretreatment with 10 g NaOH 100 g−1 of VS of CM, and a temperature of 45 °C. It was found that the CM pretreatment with NaOH substantially increases the energy profile of the WtE-AD process without compromising the environmental impact since greenhouse gas emissions in chemical production are negligible.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".